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The first requirement of crop improvement through genetic modification is the discovery of efficacious transgenes. Screening experiments with transgenic plants are a direct approach to this end. This thesis describes investigations that aimed at the improvement of these experiments and of the analysis of data they provide. The solutions to this aim need to consider the constraints and specificities of transgenic plant material and the requirements of high throughput, of minimized costs and of short time lines. The statistical models need to accommodate the specific structure of a population of plants obtained after introducing a foreign gene in a plant genome. The analysis needs to allow inference about commercial potential of the transgene, despite being based on experiments with only non-commercializable plant material. A general introduction to screening of transgenes with the goal to improve crop yield is provided in order to explain the constraints and peculiarities of this application area. In the first steps of this process, phenotypes of plants that received a transgene are observed and measured in experiments set up for that purpose. The analysis of these phenotyping experiments for transgenics differs from the analysis as it is usually applied in similar screening experiments in the context of traditional breeding. There are additional complexity and sources of variation and the inference has different aims. Transgenes are inserted in the original plant genome but cannot be directed to a specific spot in the genome. Expression levels of the transgene differ considerably among insertions, because some may have been inserted in a non-transcribed part of the genome (and hence have no effect) or because they have disrupted a functional endogenous gene. Agronomically most interesting insertions may have an intermediate level of transgene expression. Therefore, observations need to be made on many insertion events and studied how their effect on the phenotype differs. All off-spring originating from an insertion forms an event. An elite event is a successful insertion of an active transgene. Many insertions need to be screened to detect such an elite event. This is expensive, hence optimization of the process is needed. The screening of events in a commercial crop is usually preceded by the screening of genes in an easily transformable test plant. This first phase of the research mainly aims at the discovery of which genes have a phenotypic effect. When an effective gene is found, the results from the first phase can have additional uses, like the prediction of the probability of finding elite events with similar or larger effects in the commercial crop.Experiments set up for the detection of interesting phenotypes of transgenic plants have to screen therefore several insertions of a particular transgene. Experimental design and the analysis of the data have to be able to detect small differences observed for quantitative traits, but also have to focus on the variability among insertions and be able to distinguish several specific sources of variation inherent to this transgene screening. The information about the variability is used to make inference about the entire population of insertions, and helps to predict the potential of the transgene for commercial crop development.The mixed model framework is an adequate approach to analyze data of phenotyping experiments because it allows to model the specific structure of the tested transgenic plant population and to study sources and magnitudes of variability. The thesis explains a particular mixed model extensively and illustrates its use on real data sets and simulated data. It allows for variance and covariance structures that are dictated by the way plants are grouped as they belong to the off-spring of particular insertions. It provides a way to incorporate nullizygotes (i.e. transgenics that lost the transgene by Mendelian segregation) as insertion-specific control plants. It estimates important variance components that permit broader inferences. It also helps to optimize the experimental set-up of subsequent experiments for further insertion selection. An important corollary of these studies is that in order to be useful for predictions outside the actually tested insertions, early screening experiments need to involve a large number of insertions, typically more than 30. When it comes to resource management, the number of insertions is much more important than the number of plants within event.Due to different levels of expression among insertions, a particular efficacious transgene will not in all insertions lead to a desired phenotype. In the population of tested insertions, not all insertions may therefore reflect the potential of the transgene. As a consequence, the average performance over all insertions is not a good predictor of the transgene performance. We investigated other metrics that focus more on the positive effects among insertions, in particular quantiles of the insertion effects distribution. The methods are described and their performance predicted based on the assumptions these methods rely on. The methods have been applied to real data sets and to simulated data sets to verify the predicted performance. Practical recommendations are formulated concerning experimental setup and analysis. All calculations were carried out in R and code is available.

Academic collection --- 575.08:577.21 --- 66.098 --- 631.52 --- Genetic engineering, genetic manipulation, recombinant DNA --- Biological processes. Biotechnology --- Improvement of plant strains. Applied genetics. Selection etc. --- Theses --- 631.52 Improvement of plant strains. Applied genetics. Selection etc. --- 66.098 Biological processes. Biotechnology --- 575.08:577.21 Genetic engineering, genetic manipulation, recombinant DNA

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Woody plants --- Senegal --- Casamance (Senegal) --- Handbooks, manuals, etc. --- Identification --- Trees

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